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Semi-supervised kernel regression using whitened function classes

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Kwon Y, Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Franz, M., Kwon Y, Rasmussen, C., & Schölkopf, B. (2004). Semi-supervised kernel regression using whitened function classes. In Pattern Recognition, Proceedings of the 26th DAGM Symposium (pp. 18-26).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-F37A-B
Zusammenfassung
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.